Séminaires et colloques

Machine learning in particle physics

par Dr Anja Butter (Heidelberg U.)

Europe/Paris
online only (LPSC)

online only

LPSC

Description

Over the next years, measurements at the LHC and the HL-LHC will provide us with a wealth of data. The best hope of answering fundamental questions like the nature of dark matter, is to adopt big data techniques in analyses and simulations to extract all relevant information. At the analysis level, machine learning methods have already shown impressive performance boosts in many areas like top tagging, jet calibration or particle identification. On the theory side, LHC physics crucially relies on our ability to simulate events efficiently from first principles. In the coming LHC runs, these simulations will face unprecedented precision requirements to match the experimental accuracy. The curse of dimensionality for the large parameter space challenges the efficiency of Monte Carlo generators. Innovative ML techniques, in particular generative models can help us overcome these limitations and pave the way for a deeper understanding of physics.

Organisé par

Marie-Laure Gallin-Martel, Sabine Kraml